Overview

Dataset statistics

Number of variables31
Number of observations83590
Missing cells3779
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 MiB
Average record size in memory248.0 B

Variable types

Numeric12
Categorical19

Warnings

Nationality has a high cardinality: 188 distinct values High cardinality
NameHash has a high cardinality: 80642 distinct values High cardinality
DocIDHash has a high cardinality: 76993 distinct values High cardinality
ID is highly correlated with DaysSinceCreation and 1 other fieldsHigh correlation
DaysSinceCreation is highly correlated with ID and 2 other fieldsHigh correlation
DaysSinceLastStay is highly correlated with DaysSinceCreation and 1 other fieldsHigh correlation
DaysSinceFirstStay is highly correlated with ID and 2 other fieldsHigh correlation
Age has 3779 (4.5%) missing values Missing
BookingsCanceled is highly skewed (γ1 = 58.30529732) Skewed
BookingsCheckedIn is highly skewed (γ1 = 26.8778824) Skewed
ID is uniformly distributed Uniform
NameHash is uniformly distributed Uniform
ID has unique values Unique
AverageLeadTime has 22713 (27.2%) zeros Zeros
LodgingRevenue has 20408 (24.4%) zeros Zeros
OtherRevenue has 20214 (24.2%) zeros Zeros
BookingsCanceled has 83472 (99.9%) zeros Zeros
BookingsCheckedIn has 19920 (23.8%) zeros Zeros
PersonsNights has 19922 (23.8%) zeros Zeros
RoomNights has 19920 (23.8%) zeros Zeros

Reproduction

Analysis started2021-01-07 20:18:08.146099
Analysis finished2021-01-07 20:19:07.023265
Duration58.88 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct83590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41795.5
Minimum1
Maximum83590
Zeros0
Zeros (%)0.0%
Memory size653.2 KiB
2021-01-07T21:19:07.235492image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4180.45
Q120898.25
median41795.5
Q362692.75
95-th percentile79410.55
Maximum83590
Range83589
Interquartile range (IQR)41794.5

Descriptive statistics

Standard deviation24130.49884
Coefficient of variation (CV)0.5773468158
Kurtosis-1.2
Mean41795.5
Median Absolute Deviation (MAD)20897.5
Skewness0
Sum3493685845
Variance582280974.2
MonotocityStrictly increasing
2021-01-07T21:19:07.422245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
437121
 
< 0.1%
47591
 
< 0.1%
272881
 
< 0.1%
252411
 
< 0.1%
313861
 
< 0.1%
293391
 
< 0.1%
191001
 
< 0.1%
170531
 
< 0.1%
231981
 
< 0.1%
Other values (83580)83580
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
835901
< 0.1%
835891
< 0.1%
835881
< 0.1%
835871
< 0.1%
835861
< 0.1%

Nationality
Categorical

HIGH CARDINALITY

Distinct188
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
FRA
12422 
PRT
11597 
DEU
10232 
GBR
8656 
ESP
4902 
Other values (183)
35781 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters250770
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowDEU
4th rowFRA
5th rowFRA
ValueCountFrequency (%)
FRA12422
14.9%
PRT11597
13.9%
DEU10232
12.2%
GBR8656
10.4%
ESP4902
 
5.9%
USA3429
 
4.1%
ITA3365
 
4.0%
BEL3119
 
3.7%
BRA2902
 
3.5%
NLD2725
 
3.3%
Other values (178)20241
24.2%
2021-01-07T21:19:07.969303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fra12422
14.9%
prt11597
13.9%
deu10232
12.2%
gbr8656
10.4%
esp4902
 
5.9%
usa3429
 
4.1%
ita3365
 
4.0%
bel3119
 
3.7%
bra2902
 
3.5%
nld2725
 
3.3%
Other values (178)20241
24.2%

Most occurring characters

ValueCountFrequency (%)
R42157
16.8%
A26949
10.7%
E22394
8.9%
U17969
 
7.2%
P17785
 
7.1%
T17045
 
6.8%
B15179
 
6.1%
D13985
 
5.6%
F13158
 
5.2%
S12378
 
4.9%
Other values (16)51771
20.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter250770
100.0%

Most frequent character per category

ValueCountFrequency (%)
R42157
16.8%
A26949
10.7%
E22394
8.9%
U17969
 
7.2%
P17785
 
7.1%
T17045
 
6.8%
B15179
 
6.1%
D13985
 
5.6%
F13158
 
5.2%
S12378
 
4.9%
Other values (16)51771
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin250770
100.0%

Most frequent character per script

ValueCountFrequency (%)
R42157
16.8%
A26949
10.7%
E22394
8.9%
U17969
 
7.2%
P17785
 
7.1%
T17045
 
6.8%
B15179
 
6.1%
D13985
 
5.6%
F13158
 
5.2%
S12378
 
4.9%
Other values (16)51771
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII250770
100.0%

Most frequent character per block

ValueCountFrequency (%)
R42157
16.8%
A26949
10.7%
E22394
8.9%
U17969
 
7.2%
P17785
 
7.1%
T17045
 
6.8%
B15179
 
6.1%
D13985
 
5.6%
F13158
 
5.2%
S12378
 
4.9%
Other values (16)51771
20.6%

Age
Real number (ℝ)

MISSING

Distinct105
Distinct (%)0.1%
Missing3779
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean45.39802784
Minimum-11
Maximum122
Zeros42
Zeros (%)0.1%
Memory size653.2 KiB
2021-01-07T21:19:08.111746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile16
Q134
median46
Q357
95-th percentile72
Maximum122
Range133
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.57236823
Coefficient of variation (CV)0.3650459946
Kurtosis-0.2889524197
Mean45.39802784
Median Absolute Deviation (MAD)12
Skewness-0.1596666101
Sum3623262
Variance274.6433887
MonotocityNot monotonic
2021-01-07T21:19:08.278671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502034
 
2.4%
512031
 
2.4%
541981
 
2.4%
531938
 
2.3%
491886
 
2.3%
521883
 
2.3%
481882
 
2.3%
471846
 
2.2%
551838
 
2.2%
461744
 
2.1%
Other values (95)60748
72.7%
(Missing)3779
 
4.5%
ValueCountFrequency (%)
-112
< 0.1%
-104
< 0.1%
-92
< 0.1%
-73
< 0.1%
-63
< 0.1%
ValueCountFrequency (%)
1221
 
< 0.1%
1142
< 0.1%
1133
< 0.1%
1101
 
< 0.1%
1091
 
< 0.1%

DaysSinceCreation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1095
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453.640902
Minimum0
Maximum1095
Zeros97
Zeros (%)0.1%
Memory size653.2 KiB
2021-01-07T21:19:08.479444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52
Q1177
median397
Q3723
95-th percentile998
Maximum1095
Range1095
Interquartile range (IQR)546

Descriptive statistics

Standard deviation313.3902913
Coefficient of variation (CV)0.6908334101
Kurtosis-1.143564808
Mean453.640902
Median Absolute Deviation (MAD)249
Skewness0.3968001271
Sum37919843
Variance98213.47469
MonotocityNot monotonic
2021-01-07T21:19:08.648803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212298
 
0.4%
232247
 
0.3%
22233
 
0.3%
281227
 
0.3%
101225
 
0.3%
195220
 
0.3%
78211
 
0.3%
217211
 
0.3%
206207
 
0.2%
85206
 
0.2%
Other values (1085)81305
97.3%
ValueCountFrequency (%)
097
0.1%
1121
0.1%
2196
0.2%
3111
0.1%
489
0.1%
ValueCountFrequency (%)
109570
0.1%
109490
0.1%
1093103
0.1%
109216
 
< 0.1%
109199
0.1%

NameHash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct80642
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0xD1490806AB49549565586CE26419163D5EFDD3C607B988CB7265BC16E754854B
 
47
0x52AD78E02E2A9A4F5B2D09A3509BAEF5DDA8D3F3DC4DB7AF0921C352CB7C26B8
 
18
0xF0F3DBC14E608DB89EB4A499B8DB16A8256AE765AEDFF8BC366FC64A019D1CBD
 
17
0xA7AE8E3A31EB75D6228B17AD6D699CAC13B6A37351FD31D287C13A39543FEC88
 
16
0xC397E9F80FD9A9AB7365734240F585038E9EBCCE93D875C631D0B56A35C5A59D
 
12
Other values (80637)
83480 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters5516940
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78383 ?
Unique (%)93.8%

Sample

1st row0x8E0A7AF39B633D5EA25C3B7EF4DFC5464B36DB7AF375716EB065E29697CC071E
2nd row0x21EDE41906B45079E75385B5AA33287CA09DE1AB86DE66EF88352FD1BE8DE368
3rd row0x31C5E4B74E23231295FDB724AD578C02C4A723F4BA2B4AF99F129EC2F4B3AD41
4th row0xFF534C83C0EF23D1CE516BC80A65D0197003D27937D485BC549171D52CE13CEA
5th row0x9C1DEF02C9BE242842C1C1ABF2C5AA249A1EEB4763B47FF457133EE9199F1037
ValueCountFrequency (%)
0xD1490806AB49549565586CE26419163D5EFDD3C607B988CB7265BC16E754854B47
 
0.1%
0x52AD78E02E2A9A4F5B2D09A3509BAEF5DDA8D3F3DC4DB7AF0921C352CB7C26B818
 
< 0.1%
0xF0F3DBC14E608DB89EB4A499B8DB16A8256AE765AEDFF8BC366FC64A019D1CBD17
 
< 0.1%
0xA7AE8E3A31EB75D6228B17AD6D699CAC13B6A37351FD31D287C13A39543FEC8816
 
< 0.1%
0xC397E9F80FD9A9AB7365734240F585038E9EBCCE93D875C631D0B56A35C5A59D12
 
< 0.1%
0xCC79C3E4774E3895E729768D1DE73E6340B493619BB6AAABEA218FF4A91722D511
 
< 0.1%
0xBE868EBC16F20D60E9AF5768E974E0D8EEFBBD3D6EACCE4E39F3D47809A3510E10
 
< 0.1%
0xB73465DE229AC416DDED0A4DDFADB1428922CDDFFC84FF206EA7868784E04DEA10
 
< 0.1%
0xFAA092BD2DEC4643F642489240C07CAE8311FA20C376B3D3D6B62BCA5DA7F9E110
 
< 0.1%
0x7288D12D383C94B2D140A2045711DD48205EF14F389D755763E10FEEA2AB421110
 
< 0.1%
Other values (80632)83429
99.8%
2021-01-07T21:19:09.342037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0xd1490806ab49549565586ce26419163d5efdd3c607b988cb7265bc16e754854b47
 
0.1%
0x52ad78e02e2a9a4f5b2d09a3509baef5dda8d3f3dc4db7af0921c352cb7c26b818
 
< 0.1%
0xf0f3dbc14e608db89eb4a499b8db16a8256ae765aedff8bc366fc64a019d1cbd17
 
< 0.1%
0xa7ae8e3a31eb75d6228b17ad6d699cac13b6a37351fd31d287c13a39543fec8816
 
< 0.1%
0xc397e9f80fd9a9ab7365734240f585038e9ebcce93d875c631d0b56a35c5a59d12
 
< 0.1%
0xcc79c3e4774e3895e729768d1de73e6340b493619bb6aaabea218ff4a91722d511
 
< 0.1%
0xb73465de229ac416dded0a4ddfadb1428922cddffc84ff206ea7868784e04dea10
 
< 0.1%
0x7288d12d383c94b2d140a2045711dd48205ef14f389d755763e10feea2ab421110
 
< 0.1%
0x2d2771e932895a19d4be999d2bd051f9e2b2ed0ef6f78d43892febfc460ca0d410
 
< 0.1%
0xfaa092bd2dec4643f642489240c07cae8311fa20c376b3d3d6b62bca5da7f9e110
 
< 0.1%
Other values (80632)83429
99.8%

Most occurring characters

ValueCountFrequency (%)
0417852
 
7.6%
D335672
 
6.1%
5334624
 
6.1%
2334609
 
6.1%
7334567
 
6.1%
9334548
 
6.1%
6334472
 
6.1%
B334467
 
6.1%
C334341
 
6.1%
A334328
 
6.1%
Other values (7)2087460
37.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3426711
62.1%
Uppercase Letter2006639
36.4%
Lowercase Letter83590
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
0417852
12.2%
5334624
9.8%
2334609
9.8%
7334567
9.8%
9334548
9.8%
6334472
9.8%
8334327
9.8%
4334263
9.8%
1334166
9.8%
3333283
9.7%
ValueCountFrequency (%)
D335672
16.7%
B334467
16.7%
C334341
16.7%
A334328
16.7%
E333922
16.6%
F333909
16.6%
ValueCountFrequency (%)
x83590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3426711
62.1%
Latin2090229
37.9%

Most frequent character per script

ValueCountFrequency (%)
0417852
12.2%
5334624
9.8%
2334609
9.8%
7334567
9.8%
9334548
9.8%
6334472
9.8%
8334327
9.8%
4334263
9.8%
1334166
9.8%
3333283
9.7%
ValueCountFrequency (%)
D335672
16.1%
B334467
16.0%
C334341
16.0%
A334328
16.0%
E333922
16.0%
F333909
16.0%
x83590
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5516940
100.0%

Most frequent character per block

ValueCountFrequency (%)
0417852
 
7.6%
D335672
 
6.1%
5334624
 
6.1%
2334609
 
6.1%
7334567
 
6.1%
9334548
 
6.1%
6334472
 
6.1%
B334467
 
6.1%
C334341
 
6.1%
A334328
 
6.1%
Other values (7)2087460
37.8%

DocIDHash
Categorical

HIGH CARDINALITY

Distinct76993
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0x5FA1E0098A31497057C5A6B9FE9D49FD6DD47CCE7C268E6548699E78E587AAEA
 
3657
0xFDBABC6688FD5F6B0E1CB119E1676C228066AC1545D0B4DA3B3C8B10B3091210
 
25
0x154DDC115DF524006203F0A7F59DE028542EC307149BF7F20E37B9D4502B89BD
 
18
0x101976BA2149F74CDA4CF21A2C01494D789D58168A6FEE8E9EA921C369504064
 
13
0x52911ACD341A0489025445B361A0EBF49CDC76B2D3DBC0CF6CDBF2F5DD1F5C22
 
12
Other values (76988)
79865 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters5516940
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74739 ?
Unique (%)89.4%

Sample

1st row0x71568459B729F7A7ABBED6C781A84CA4274D571003ACC7A4A791C3350D924137
2nd row0x5FA1E0098A31497057C5A6B9FE9D49FD6DD47CCE7C268E6548699E78E587AAEA
3rd row0xC7CF344F5B03295037595B1337AC905CA188F1B5B3A56C8C6E1A24202C9C672C
4th row0xBD3823A9B4EC35D6CAF4B27AE423A677C0200DB61E823EE8BE57787729DCBDB8
5th row0xE175754CF77247B202DD0820F49407C762C14A603B3A6CFEA2A4DC06A5F7E00C
ValueCountFrequency (%)
0x5FA1E0098A31497057C5A6B9FE9D49FD6DD47CCE7C268E6548699E78E587AAEA3657
 
4.4%
0xFDBABC6688FD5F6B0E1CB119E1676C228066AC1545D0B4DA3B3C8B10B309121025
 
< 0.1%
0x154DDC115DF524006203F0A7F59DE028542EC307149BF7F20E37B9D4502B89BD18
 
< 0.1%
0x101976BA2149F74CDA4CF21A2C01494D789D58168A6FEE8E9EA921C36950406413
 
< 0.1%
0x52911ACD341A0489025445B361A0EBF49CDC76B2D3DBC0CF6CDBF2F5DD1F5C2212
 
< 0.1%
0xE8675E9652E9AA6C2467707A81CA012A73AF7156A3E8D9CA22664A7DE75C588F12
 
< 0.1%
0x2907579A969DD1DD4BE224AB6B7D0F709AE9A38FBE696E9F74EDAF0EAB52D07311
 
< 0.1%
0x3C395FF01D917F9E91CBB98F1C29E7F77AE795141A7D193B6B901C6B7EFDCD4C10
 
< 0.1%
0x075E7AF0463C2AE314CD3A5E3147AC11CD4E7A36BC62E423F9C36C33D292EC0110
 
< 0.1%
0xE1A91E64B342EFEE3464A898387406EEF137B5CB346D549D06A176C04FF4ECF29
 
< 0.1%
Other values (76983)79813
95.5%
2021-01-07T21:19:10.066303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0x5fa1e0098a31497057c5a6b9fe9d49fd6dd47cce7c268e6548699e78e587aaea3657
 
4.4%
0xfdbabc6688fd5f6b0e1cb119e1676c228066ac1545d0b4da3b3c8b10b309121025
 
< 0.1%
0x154ddc115df524006203f0a7f59de028542ec307149bf7f20e37b9d4502b89bd18
 
< 0.1%
0x101976ba2149f74cda4cf21a2c01494d789d58168a6fee8e9ea921c36950406413
 
< 0.1%
0xe8675e9652e9aa6c2467707a81ca012a73af7156a3e8d9ca22664a7de75c588f12
 
< 0.1%
0x52911acd341a0489025445b361a0ebf49cdc76b2d3dbc0cf6cdbf2f5dd1f5c2212
 
< 0.1%
0x2907579a969dd1dd4be224ab6b7d0f709ae9a38fbe696e9f74edaf0eab52d07311
 
< 0.1%
0x075e7af0463c2ae314cd3a5e3147ac11cd4e7a36bc62e423f9c36c33d292ec0110
 
< 0.1%
0x3c395ff01d917f9e91cbb98f1c29e7f77ae795141a7d193b6b901c6b7efdcd4c10
 
< 0.1%
0x632a5f8e91e5e52bd383fa6592e1a7245d499bf711c523fc4061dcdd31cfbe1c9
 
< 0.1%
Other values (76983)79813
95.5%

Most occurring characters

ValueCountFrequency (%)
0412740
 
7.5%
E345560
 
6.3%
9344642
 
6.2%
7342962
 
6.2%
A341978
 
6.2%
6338047
 
6.1%
8337895
 
6.1%
5337876
 
6.1%
4335654
 
6.1%
D334710
 
6.1%
Other values (7)2044876
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3423671
62.1%
Uppercase Letter2009679
36.4%
Lowercase Letter83590
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
0412740
12.1%
9344642
10.1%
7342962
10.0%
6338047
9.9%
8337895
9.9%
5337876
9.9%
4335654
9.8%
1327012
9.6%
3323911
9.5%
2322932
9.4%
ValueCountFrequency (%)
E345560
17.2%
A341978
17.0%
D334710
16.7%
C334223
16.6%
F330325
16.4%
B322883
16.1%
ValueCountFrequency (%)
x83590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3423671
62.1%
Latin2093269
37.9%

Most frequent character per script

ValueCountFrequency (%)
0412740
12.1%
9344642
10.1%
7342962
10.0%
6338047
9.9%
8337895
9.9%
5337876
9.9%
4335654
9.8%
1327012
9.6%
3323911
9.5%
2322932
9.4%
ValueCountFrequency (%)
E345560
16.5%
A341978
16.3%
D334710
16.0%
C334223
16.0%
F330325
15.8%
B322883
15.4%
x83590
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5516940
100.0%

Most frequent character per block

ValueCountFrequency (%)
0412740
 
7.5%
E345560
 
6.3%
9344642
 
6.2%
7342962
 
6.2%
A341978
 
6.2%
6338047
 
6.1%
8337895
 
6.1%
5337876
 
6.1%
4335654
 
6.1%
D334710
 
6.1%
Other values (7)2044876
37.1%

AverageLeadTime
Real number (ℝ)

ZEROS

Distinct418
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.19602823
Minimum-1
Maximum588
Zeros22713
Zeros (%)27.2%
Memory size653.2 KiB
2021-01-07T21:19:10.228625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median29
Q3103
95-th percentile240
Maximum588
Range589
Interquartile range (IQR)103

Descriptive statistics

Standard deviation87.75898964
Coefficient of variation (CV)1.325744036
Kurtosis4.479480574
Mean66.19602823
Median Absolute Deviation (MAD)29
Skewness1.910365214
Sum5533326
Variance7701.640262
MonotocityNot monotonic
2021-01-07T21:19:10.433139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022713
27.2%
11705
 
2.0%
21057
 
1.3%
61045
 
1.3%
51018
 
1.2%
41016
 
1.2%
3979
 
1.2%
7957
 
1.1%
8906
 
1.1%
9675
 
0.8%
Other values (408)51519
61.6%
ValueCountFrequency (%)
-110
 
< 0.1%
022713
27.2%
11705
 
2.0%
21057
 
1.3%
3979
 
1.2%
ValueCountFrequency (%)
58819
< 0.1%
57410
< 0.1%
54922
< 0.1%
54610
< 0.1%
5432
 
< 0.1%

LodgingRevenue
Real number (ℝ≥0)

ZEROS

Distinct10257
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.8020871
Minimum0
Maximum21781
Zeros20408
Zeros (%)24.4%
Memory size653.2 KiB
2021-01-07T21:19:10.609237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q159
median234
Q3402
95-th percentile890
Maximum21781
Range21781
Interquartile range (IQR)343

Descriptive statistics

Standard deviation372.8518922
Coefficient of variation (CV)1.247822248
Kurtosis179.1061127
Mean298.8020871
Median Absolute Deviation (MAD)170.96
Skewness6.57726458
Sum24976866.46
Variance139018.5335
MonotocityNot monotonic
2021-01-07T21:19:10.778978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020408
 
24.4%
126636
 
0.8%
234591
 
0.7%
249507
 
0.6%
89371
 
0.4%
210319
 
0.4%
342295
 
0.4%
178293
 
0.4%
128250
 
0.3%
258240
 
0.3%
Other values (10247)59680
71.4%
ValueCountFrequency (%)
020408
24.4%
182
 
< 0.1%
221
 
< 0.1%
245
 
< 0.1%
251
 
< 0.1%
ValueCountFrequency (%)
217811
< 0.1%
9682.41
< 0.1%
9665.661
< 0.1%
91801
< 0.1%
90101
< 0.1%

OtherRevenue
Real number (ℝ≥0)

ZEROS

Distinct4490
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.58913183
Minimum0
Maximum7730.25
Zeros20214
Zeros (%)24.2%
Memory size653.2 KiB
2021-01-07T21:19:10.950189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median38.5
Q387.675
95-th percentile237.81
Maximum7730.25
Range7730.25
Interquartile range (IQR)85.675

Descriptive statistics

Standard deviation114.3277758
Coefficient of variation (CV)1.691511234
Kurtosis383.0458592
Mean67.58913183
Median Absolute Deviation (MAD)38.5
Skewness10.82256449
Sum5649775.53
Variance13070.84032
MonotocityNot monotonic
2021-01-07T21:19:11.133968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020214
24.2%
422968
 
3.6%
142850
 
3.4%
282227
 
2.7%
71730
 
2.1%
561666
 
2.0%
211288
 
1.5%
2959
 
1.1%
6753
 
0.9%
84743
 
0.9%
Other values (4480)48192
57.7%
ValueCountFrequency (%)
020214
24.2%
1369
 
0.4%
1.91
 
< 0.1%
2959
 
1.1%
2.13
 
< 0.1%
ValueCountFrequency (%)
7730.251
< 0.1%
5105.51
< 0.1%
42961
< 0.1%
3580.51
< 0.1%
2814.81
< 0.1%

BookingsCanceled
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002021772939
Minimum0
Maximum9
Zeros83472
Zeros (%)99.9%
Memory size653.2 KiB
2021-01-07T21:19:11.284519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06676991852
Coefficient of variation (CV)33.02542893
Kurtosis5270.818134
Mean0.002021772939
Median Absolute Deviation (MAD)0
Skewness58.30529732
Sum169
Variance0.004458222019
MonotocityNot monotonic
2021-01-07T21:19:11.402988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
083472
99.9%
192
 
0.1%
212
 
< 0.1%
38
 
< 0.1%
45
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
083472
99.9%
192
 
0.1%
212
 
< 0.1%
38
 
< 0.1%
45
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
45
 
< 0.1%
38
 
< 0.1%
212
 
< 0.1%
192
0.1%

BookingsNoShowed
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83546 
1
 
36
2
 
7
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083546
99.9%
136
 
< 0.1%
27
 
< 0.1%
31
 
< 0.1%
2021-01-07T21:19:11.648971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:11.727342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083546
99.9%
136
 
< 0.1%
27
 
< 0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
083546
99.9%
136
 
< 0.1%
27
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083546
99.9%
136
 
< 0.1%
27
 
< 0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083546
99.9%
136
 
< 0.1%
27
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083546
99.9%
136
 
< 0.1%
27
 
< 0.1%
31
 
< 0.1%

BookingsCheckedIn
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7946165809
Minimum0
Maximum66
Zeros19920
Zeros (%)23.8%
Memory size653.2 KiB
2021-01-07T21:19:11.831749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum66
Range66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6957776159
Coefficient of variation (CV)0.8756142681
Kurtosis1836.955175
Mean0.7946165809
Median Absolute Deviation (MAD)0
Skewness26.8778824
Sum66422
Variance0.4841064908
MonotocityNot monotonic
2021-01-07T21:19:11.967151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
162215
74.4%
019920
 
23.8%
21147
 
1.4%
3132
 
0.2%
459
 
0.1%
520
 
< 0.1%
620
 
< 0.1%
716
 
< 0.1%
810
 
< 0.1%
99
 
< 0.1%
Other values (19)42
 
0.1%
ValueCountFrequency (%)
019920
 
23.8%
162215
74.4%
21147
 
1.4%
3132
 
0.2%
459
 
0.1%
ValueCountFrequency (%)
661
 
< 0.1%
571
 
< 0.1%
401
 
< 0.1%
341
 
< 0.1%
293
< 0.1%

PersonsNights
Real number (ℝ≥0)

ZEROS

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.649132671
Minimum0
Maximum116
Zeros19922
Zeros (%)23.8%
Memory size653.2 KiB
2021-01-07T21:19:12.113224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile12
Maximum116
Range116
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.567672855
Coefficient of variation (CV)0.9824784918
Kurtosis12.44153528
Mean4.649132671
Median Absolute Deviation (MAD)3
Skewness1.92920906
Sum388621
Variance20.86363531
MonotocityNot monotonic
2021-01-07T21:19:12.324859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019922
23.8%
613704
16.4%
410418
12.5%
29628
11.5%
88249
9.9%
14145
 
5.0%
34018
 
4.8%
103468
 
4.1%
123091
 
3.7%
91845
 
2.2%
Other values (46)5102
 
6.1%
ValueCountFrequency (%)
019922
23.8%
14145
 
5.0%
29628
11.5%
34018
 
4.8%
410418
12.5%
ValueCountFrequency (%)
1161
< 0.1%
781
< 0.1%
751
< 0.1%
731
< 0.1%
682
< 0.1%

RoomNights
Real number (ℝ≥0)

ZEROS

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.35853571
Minimum0
Maximum185
Zeros19920
Zeros (%)23.8%
Memory size653.2 KiB
2021-01-07T21:19:12.496091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum185
Range185
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.281745625
Coefficient of variation (CV)0.967441627
Kurtosis647.7046043
Mean2.35853571
Median Absolute Deviation (MAD)1
Skewness11.19024256
Sum197150
Variance5.206363095
MonotocityNot monotonic
2021-01-07T21:19:12.656473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
019920
23.8%
317149
20.5%
214115
16.9%
111381
13.6%
411158
13.3%
55050
 
6.0%
71937
 
2.3%
61853
 
2.2%
8372
 
0.4%
9198
 
0.2%
Other values (38)457
 
0.5%
ValueCountFrequency (%)
019920
23.8%
111381
13.6%
214115
16.9%
317149
20.5%
411158
13.3%
ValueCountFrequency (%)
1851
< 0.1%
1161
< 0.1%
951
< 0.1%
881
< 0.1%
661
< 0.1%

DaysSinceLastStay
Real number (ℝ)

HIGH CORRELATION

Distinct1105
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean401.0671372
Minimum-1
Maximum1104
Zeros1
Zeros (%)< 0.1%
Memory size653.2 KiB
2021-01-07T21:19:12.828771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q126
median366
Q3693
95-th percentile981
Maximum1104
Range1105
Interquartile range (IQR)667

Descriptive statistics

Standard deviation347.2049554
Coefficient of variation (CV)0.8657028293
Kurtosis-1.285179325
Mean401.0671372
Median Absolute Deviation (MAD)333
Skewness0.3099888192
Sum33525202
Variance120551.2811
MonotocityNot monotonic
2021-01-07T21:19:13.016651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-119920
 
23.8%
920203
 
0.2%
472196
 
0.2%
477165
 
0.2%
938158
 
0.2%
97156
 
0.2%
192144
 
0.2%
217144
 
0.2%
206126
 
0.2%
442126
 
0.2%
Other values (1095)62252
74.5%
ValueCountFrequency (%)
-119920
23.8%
01
 
< 0.1%
14
 
< 0.1%
224
 
< 0.1%
342
 
0.1%
ValueCountFrequency (%)
11043
 
< 0.1%
11021
 
< 0.1%
11012
 
< 0.1%
110012
< 0.1%
109912
< 0.1%

DaysSinceFirstStay
Real number (ℝ)

HIGH CORRELATION

Distinct1108
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.349013
Minimum-1
Maximum1186
Zeros0
Zeros (%)0.0%
Memory size653.2 KiB
2021-01-07T21:19:13.190551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q127
median369
Q3697
95-th percentile982
Maximum1186
Range1187
Interquartile range (IQR)670

Descriptive statistics

Standard deviation347.9710894
Coefficient of variation (CV)0.8627047002
Kurtosis-1.292214215
Mean403.349013
Median Absolute Deviation (MAD)336
Skewness0.3011868156
Sum33715944
Variance121083.879
MonotocityNot monotonic
2021-01-07T21:19:13.373685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-119920
 
23.8%
472203
 
0.2%
920203
 
0.2%
477161
 
0.2%
938157
 
0.2%
97149
 
0.2%
217140
 
0.2%
192139
 
0.2%
442125
 
0.1%
918124
 
0.1%
Other values (1098)62269
74.5%
ValueCountFrequency (%)
-119920
23.8%
12
 
< 0.1%
223
 
< 0.1%
342
 
0.1%
469
 
0.1%
ValueCountFrequency (%)
11861
 
< 0.1%
11171
 
< 0.1%
11161
 
< 0.1%
11111
 
< 0.1%
11043
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
Travel Agent/Operator
68569 
Direct
11912 
Corporate
 
2600
Electronic Distribution
 
509

Length

Max length23
Median length21
Mean length18.50135184
Min length6

Characters and Unicode

Total characters1546528
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator
ValueCountFrequency (%)
Travel Agent/Operator68569
82.0%
Direct11912
 
14.3%
Corporate2600
 
3.1%
Electronic Distribution509
 
0.6%
2021-01-07T21:19:14.371806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:14.482083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
travel68569
44.9%
agent/operator68569
44.9%
direct11912
 
7.8%
corporate2600
 
1.7%
distribution509
 
0.3%
electronic509
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r223837
14.5%
e220728
14.3%
t153177
 
9.9%
a139738
 
9.0%
o74787
 
4.8%
p71169
 
4.6%
n69587
 
4.5%
l69078
 
4.5%
69078
 
4.5%
T68569
 
4.4%
Other values (13)386780
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1187644
76.8%
Uppercase Letter221237
 
14.3%
Space Separator69078
 
4.5%
Other Punctuation68569
 
4.4%

Most frequent character per category

ValueCountFrequency (%)
r223837
18.8%
e220728
18.6%
t153177
12.9%
a139738
11.8%
o74787
 
6.3%
p71169
 
6.0%
n69587
 
5.9%
l69078
 
5.8%
v68569
 
5.8%
g68569
 
5.8%
Other values (5)28405
 
2.4%
ValueCountFrequency (%)
T68569
31.0%
A68569
31.0%
O68569
31.0%
D12421
 
5.6%
C2600
 
1.2%
E509
 
0.2%
ValueCountFrequency (%)
69078
100.0%
ValueCountFrequency (%)
/68569
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1408881
91.1%
Common137647
 
8.9%

Most frequent character per script

ValueCountFrequency (%)
r223837
15.9%
e220728
15.7%
t153177
10.9%
a139738
9.9%
o74787
 
5.3%
p71169
 
5.1%
n69587
 
4.9%
l69078
 
4.9%
T68569
 
4.9%
v68569
 
4.9%
Other values (11)249642
17.7%
ValueCountFrequency (%)
69078
50.2%
/68569
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1546528
100.0%

Most frequent character per block

ValueCountFrequency (%)
r223837
14.5%
e220728
14.3%
t153177
 
9.9%
a139738
 
9.0%
o74787
 
4.8%
p71169
 
4.6%
n69587
 
4.5%
l69078
 
4.5%
69078
 
4.5%
T68569
 
4.4%
Other values (13)386780
25.0%

MarketSegment
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
Other
48039 
Travel Agent/Operator
11670 
Direct
11457 
Groups
9501 
Corporate
 
2169
Other values (2)
 
754

Length

Max length21
Median length5
Mean length7.645842804
Min length5

Characters and Unicode

Total characters639116
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator
ValueCountFrequency (%)
Other48039
57.5%
Travel Agent/Operator11670
 
14.0%
Direct11457
 
13.7%
Groups9501
 
11.4%
Corporate2169
 
2.6%
Complementary510
 
0.6%
Aviation244
 
0.3%
2021-01-07T21:19:14.776199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:14.881925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
other48039
50.4%
travel11670
 
12.3%
agent/operator11670
 
12.3%
direct11457
 
12.0%
groups9501
 
10.0%
corporate2169
 
2.3%
complementary510
 
0.5%
aviation244
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r108855
17.0%
e97695
15.3%
t85759
13.4%
O59709
9.3%
h48039
 
7.5%
o26263
 
4.1%
a26263
 
4.1%
p23850
 
3.7%
n12424
 
1.9%
l12180
 
1.9%
Other values (15)138079
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter508846
79.6%
Uppercase Letter106930
 
16.7%
Space Separator11670
 
1.8%
Other Punctuation11670
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
r108855
21.4%
e97695
19.2%
t85759
16.9%
h48039
9.4%
o26263
 
5.2%
a26263
 
5.2%
p23850
 
4.7%
n12424
 
2.4%
l12180
 
2.4%
i11945
 
2.3%
Other values (7)55573
10.9%
ValueCountFrequency (%)
O59709
55.8%
A11914
 
11.1%
T11670
 
10.9%
D11457
 
10.7%
G9501
 
8.9%
C2679
 
2.5%
ValueCountFrequency (%)
11670
100.0%
ValueCountFrequency (%)
/11670
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin615776
96.3%
Common23340
 
3.7%

Most frequent character per script

ValueCountFrequency (%)
r108855
17.7%
e97695
15.9%
t85759
13.9%
O59709
9.7%
h48039
7.8%
o26263
 
4.3%
a26263
 
4.3%
p23850
 
3.9%
n12424
 
2.0%
l12180
 
2.0%
Other values (13)114739
18.6%
ValueCountFrequency (%)
11670
50.0%
/11670
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII639116
100.0%

Most frequent character per block

ValueCountFrequency (%)
r108855
17.0%
e97695
15.3%
t85759
13.4%
O59709
9.3%
h48039
 
7.5%
o26263
 
4.1%
a26263
 
4.1%
p23850
 
3.7%
n12424
 
1.9%
l12180
 
1.9%
Other values (15)138079
21.6%

SRHighFloor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
79621 
1
 
3969

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
079621
95.3%
13969
 
4.7%
2021-01-07T21:19:15.211147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:15.309941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
079621
95.3%
13969
 
4.7%

Most occurring characters

ValueCountFrequency (%)
079621
95.3%
13969
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
079621
95.3%
13969
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
079621
95.3%
13969
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
079621
95.3%
13969
 
4.7%

SRLowFloor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83472 
1
 
118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083472
99.9%
1118
 
0.1%
2021-01-07T21:19:15.582755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:15.684609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083472
99.9%
1118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
083472
99.9%
1118
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083472
99.9%
1118
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083472
99.9%
1118
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083472
99.9%
1118
 
0.1%

SRAccessibleRoom
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83569 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083569
> 99.9%
121
 
< 0.1%
2021-01-07T21:19:16.038266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:16.141310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083569
> 99.9%
121
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
083569
> 99.9%
121
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083569
> 99.9%
121
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083569
> 99.9%
121
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083569
> 99.9%
121
 
< 0.1%

SRMediumFloor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83517 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083517
99.9%
173
 
0.1%
2021-01-07T21:19:16.371844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:16.462512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083517
99.9%
173
 
0.1%

Most occurring characters

ValueCountFrequency (%)
083517
99.9%
173
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083517
99.9%
173
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083517
99.9%
173
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083517
99.9%
173
 
0.1%

SRBathtub
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83352 
1
 
238

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083352
99.7%
1238
 
0.3%
2021-01-07T21:19:16.723005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:16.805761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083352
99.7%
1238
 
0.3%

Most occurring characters

ValueCountFrequency (%)
083352
99.7%
1238
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083352
99.7%
1238
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083352
99.7%
1238
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083352
99.7%
1238
 
0.3%

SRShower
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83447 
1
 
143

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083447
99.8%
1143
 
0.2%
2021-01-07T21:19:17.030091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:17.112513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083447
99.8%
1143
 
0.2%

Most occurring characters

ValueCountFrequency (%)
083447
99.8%
1143
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083447
99.8%
1143
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083447
99.8%
1143
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083447
99.8%
1143
 
0.2%

SRCrib
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
82485 
1
 
1105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
082485
98.7%
11105
 
1.3%
2021-01-07T21:19:17.555302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:17.683577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
082485
98.7%
11105
 
1.3%

Most occurring characters

ValueCountFrequency (%)
082485
98.7%
11105
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
082485
98.7%
11105
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
082485
98.7%
11105
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
082485
98.7%
11105
 
1.3%

SRKingSizeBed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
54109 
1
29481 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
054109
64.7%
129481
35.3%
2021-01-07T21:19:17.984510image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:18.074682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
054109
64.7%
129481
35.3%

Most occurring characters

ValueCountFrequency (%)
054109
64.7%
129481
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
054109
64.7%
129481
35.3%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
054109
64.7%
129481
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
054109
64.7%
129481
35.3%

SRTwinBed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
71675 
1
11915 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
071675
85.7%
111915
 
14.3%
2021-01-07T21:19:18.424255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:18.519926image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
071675
85.7%
111915
 
14.3%

Most occurring characters

ValueCountFrequency (%)
071675
85.7%
111915
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
071675
85.7%
111915
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
071675
85.7%
111915
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
071675
85.7%
111915
 
14.3%

SRNearElevator
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83562 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083562
> 99.9%
128
 
< 0.1%
2021-01-07T21:19:18.733298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:18.818268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083562
> 99.9%
128
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
083562
> 99.9%
128
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083562
> 99.9%
128
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083562
> 99.9%
128
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083562
> 99.9%
128
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83297 
1
 
293

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083297
99.6%
1293
 
0.4%
2021-01-07T21:19:19.035561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:19.117717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083297
99.6%
1293
 
0.4%

Most occurring characters

ValueCountFrequency (%)
083297
99.6%
1293
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083297
99.6%
1293
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083297
99.6%
1293
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083297
99.6%
1293
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
83580 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
083580
> 99.9%
110
 
< 0.1%
2021-01-07T21:19:19.351007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:19.511197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
083580
> 99.9%
110
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
083580
> 99.9%
110
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
083580
> 99.9%
110
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
083580
> 99.9%
110
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
083580
> 99.9%
110
 
< 0.1%

SRQuietRoom
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size653.2 KiB
0
76203 
1
 
7387

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
076203
91.2%
17387
 
8.8%
2021-01-07T21:19:19.824777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-07T21:19:19.908672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
076203
91.2%
17387
 
8.8%

Most occurring characters

ValueCountFrequency (%)
076203
91.2%
17387
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number83590
100.0%

Most frequent character per category

ValueCountFrequency (%)
076203
91.2%
17387
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common83590
100.0%

Most frequent character per script

ValueCountFrequency (%)
076203
91.2%
17387
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII83590
100.0%

Most frequent character per block

ValueCountFrequency (%)
076203
91.2%
17387
 
8.8%

Interactions

2021-01-07T21:18:44.036178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-07T21:18:44.334948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-07T21:18:44.478456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-07T21:18:44.746870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-07T21:18:45.156680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-07T21:19:03.046746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-07T21:19:03.334406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-07T21:19:03.487711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-07T21:19:03.666067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-07T21:19:20.127194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-07T21:19:20.550450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-07T21:19:20.957771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-07T21:19:21.453561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-07T21:19:21.892473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-07T21:19:04.324096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-07T21:19:05.595808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-07T21:19:06.635832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDNationalityAgeDaysSinceCreationNameHashDocIDHashAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDaysSinceLastStayDaysSinceFirstStayDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
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89FRA42.010950xD9D899DA4FB0CF23FDF902C1B237A30AE854FFBC79FC67092F2E6358FF5E93080xCCDDA9F399058BA00C9A53C107F986F1B5CECD1CEAEF234DF0EF11D769445AD700.00.000000-1-1Travel Agent/OperatorOther0000000100000
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Last rows

IDNationalityAgeDaysSinceCreationNameHashDocIDHashAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDaysSinceLastStayDaysSinceFirstStayDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
8358083581DEU48.000x98FF1A89EAFB3D2A757C073C0FB89647DD33EDB97B8DF0E8B94EADC86F2BD1D40x9A75BA8B885B4CCF562FEC991F4B34DDECE67D43DD042F021C5EFB48077BC15E20176.0147.00014222Travel Agent/OperatorOther0000000000000
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